两相流相空间多元图重心轨迹动力学特征  

Dynamic characteristics of multivariate graph centrobaric trajectory in phase space of two-phase flow

作　　者：赵俊英[1,2] 金宁德[1] 

机构地区：[1]天津大学电气与自动化工程学院,天津300072 [2]天津电子信息职业技术学院电子技术系,天津300350

出　　处：《物理学报》2012年第9期331-338,共8页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:50974095;41174109);国家科技重大专项(批准号:2011ZX05020-006)资助的课题~~

摘　　要：本文提出了一种新的混沌时间序列高维相空间多元图重心轨迹动力学特征提取方法.在确定了最佳嵌入维数和延迟时间后,将相空间中高维矢量点映射到二维平面的雷达图上,相应地将相空间中高维矢量点变换为对应的几何多边形,通过提取几何多边形的重心位置得到重心轨迹动力学演化特性,并利用重心轨迹矩特征量区分不同性质的混沌时间序列.在此基础上,处理分析了气液两相流电导传感器动态信号,发现高维相空间多元图重心轨迹矩特征量不仅可以辨识泡状流、段塞流和混状流,而且为流型动力学演化机理提供了新的分析途径.We propose a multivariate graph centrobaric trajectory-based method for characterizing nonlinear dynamics from high- dimensional chaotic time series. After the optimal selecting of the embedding dimension and time delay, we map the high-dimensional vector point into the two-dimensional radial plane graph, i.e., the high-dimensional vector point is transformed correspondingly to a geometric polygon. By extracting the geometric location of the polygon barycenters, we can obtain the evolving feature of the barycen- ter dynamical trajectory. Then we use the moment quantity of the barycenter trajectory to distinguish different chaotic time series. Finally, we apply our method to the fluctuating signals measured from gas-liquid two-phase flow experiments. The results suggest that our method can be a powerful tool for not only distinguishing the different flow patterns but also investigating the dynamical evolving mechanism of flow patterns.

关 键 词：两相流 高维相空间 重心轨迹 流型动力学 

分 类 号：O359[理学—流体力学]